<h2 dir="ltr" id="isPasted">Enabling Effortless AI Transition to the Edge</h2><p dir="ltr">The global market for edge AI is estimated to reach <a href="https://pages.edgeimpulse.com/5-rising-trends-for-ai-adoption-in-manufacturing">$143.6 billion by 2032</a>. This growing adoption emphasizes the need for businesses to adopt edge-based machine learning solutions. Companies that adopt edge AI will gain a competitive edge, especially those that invest in advanced neural network hardware.&nbsp;</p><p dir="ltr">BrainChip has introduced its <a href="https://doc.brainchipinc.com/index.html">MetaTF</a> Software Tools to simplify the migration of ML models to the Akida event-based computing platform. MetaTF offers a set of tools for converting existing models so they can work with BrainChip's Akida processors.</p><p dir="ltr">Key features of MetaTF include:</p><ul><li dir="ltr"><p dir="ltr">Effortless Model Transition: It offers tools to convert, optimize, and deploy ML models for Akida processors.</p></li><li dir="ltr"><p dir="ltr">Reduced Computational Load: Akida computing focuses only on relevant event data. This helps to reduce computational overhead as compared to traditional NPUs.</p></li><li dir="ltr"><p dir="ltr">Familiar Development Environment: It allows developers to work in familiar environments like Jupyter Notebooks and experiment with ML on the edge. This can help to minimize the learning curve.</p></li><li dir="ltr"><p dir="ltr">Enhanced Model Validation: It offers a simple pathway for companies to test and refine their models on advanced processors.</p></li></ul><p dir="ltr">With these capabilities, MetaTF helps companies to adopt edge AI quickly and efficiently. It also helps them find new opportunities for innovation and performance.</p><h2 dir="ltr">The Unique Challenges of Embedded Devices</h2><p dir="ltr">Deploying ML solutions on embedded devices comes with challenges that are different from traditional server. Embedded and IoT devices, ranging from sensors and smart phones, are highly diverse. They do not work like servers that follow standard architectures. These devices have their own architecture and operating systems. Also, they may sometimes have no OS.</p><p dir="ltr">This diversity makes installing and packaging software more complex, especially when they have to scale it to multi-million devices. Software-build technologies are important to address these challenges. They:</p><ul><li dir="ltr"><p dir="ltr">Simplify the management of complex hardware configurations.</p></li><li dir="ltr"><p dir="ltr">Ensure repeatability in software deployments.</p></li><li dir="ltr"><p dir="ltr">Enable the creation of consistent images throughout different devices.</p></li></ul><p dir="ltr">These tools are important for overcoming the unique challenges of deploying ML solutions in embedded environments.</p><h2 dir="ltr">Simplifying Edge Deployment with Edge Impulse and MetaTF</h2><p dir="ltr">Training ML models for compact, low-power devices is only part of the challenge. Packaging and deploying these models on edge devices present additional hurdles. BrainChip has significant experience with popular embedded systems like <a href="https://buildroot.org/">Buildroot&nbsp;</a>and <a href="https://www.yoctoproject.org/">Yocto</a>. BrainChip Akida System on Chip (SOC) integrates smoothly into different embedding environments of customers.</p><p dir="ltr">By joining hands with <a href="https://edgeimpulse.com/">Edge Impulse</a>, BrainChip simplifies ML workflows to edge. It also simplify the deployment of event-based AI solutions. Together, they make it easier than ever to implement event-based ML solutions on embedded devices.</p><p dir="ltr">One standout example of this collaboration is the <a href="https://shop.brainchipinc.com/products/akida%E2%84%A2-edge-ai-box">Akida™ Edge Box</a>. This was created by cooperation with Edge Impulse and <a href="https://www.vvdntech.com/vision/akida-edge-ai-box">VVDN</a>. This solution showcases BrainChip’s expertise in addressing the unique challenges of edge AI deployment by integrating advanced technologies. The key features of Edge Box include:</p><ul><li dir="ltr"><p dir="ltr">Hardware Configuration: Built on an NXP i.MX 8M application processor. The Edge Box includes dual Akida 1000 chips integrated via a single M.2 PCI card.</p></li><li dir="ltr"><p dir="ltr">Streamlined Deployment: MetaTF libraries and NXP’s i.MX Yocto Project Board Support Package help integrate models trained in Edge Impulse Studio.</p></li><li dir="ltr"><p dir="ltr">Real-World Applications: It shows how event computing and embedded system tools can work together for better AI solutions.</p></li></ul><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1732020180615-Akida_Examples-1200x627.jpg" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><a href="https://brainchip.com/contact/">Contact BrainChip's architecture team</a> for more information on integrating BrainChip’s event-based AI into embedded systems or to discover the potential of the Akida Edge Box.&nbsp;</p><p dir="ltr">Bring your AI initiatives to the edge with BrainChip!</p>

How BrainChip's Akida™ Edge Box Simplifies ML Deployment


Brainchip MetaTF Software Tools ease the Transition of ML Models to the Edge

<p dir="ltr" id="isPasted">The prestigious <a href="https://www.merckgroup.com/en/research/grants-and-awards/heinrich-emanuel-merck-award.html" target="_blank">Heinrich Emanuel Merck Award</a> is a biannial recognition of individuals who have made outstanding contributions to the advancement of artificial intelligence, quantum computing, neuro-inspired computing, and their applications to enable scientific discovery. The recipient of the award will be given a €15,000 prize and will be invited to present their research at an award ceremony.&nbsp;</p><p dir="ltr">The Heinrich Emanuel Merck Award is given in memory of the company’s founder, Heinrich Emanuel Merck, and has been a recognition of excellence and innovation in research for over two decades. Originally established to recognize research in the analytical sciences and presented as such until 2022, the award has undergone a significant transformation. As of 2024, the focus of the award has shifted to computational science.&nbsp;</p><p dir="ltr">Describing the significance of this award, Dr. Ulrich Betz, Senior Vice President Innovation at Merck said:</p><blockquote><p dir="ltr">“Computational sciences are increasingly making contributions to enable new scientific discoveries in all major fields. With the new Heinrich Emanuel Merck Award we want to honor scientists that enhance and apply these modern digital technologies to enhance scientific discovery.”</p></blockquote><h2 dir="ltr">Who is eligible to apply?</h2><p dir="ltr">Previous award laureates include top researchers whose work has provided the foundation for groundbreaking discoveries. Are you or do you know someone who is developing innovative solutions in domains at the forefront of innovation in computing like artificial intelligence, quantum computing, or neuro-inspired computing? Visit the application portal and submit your application!</p><p dir="ltr"><a href="https://app.bitly.com/Bi6chcjWpnI/links?query=https://wevlv.co/HEMAportal" target="_blank" rel="noopener noreferrer" id="isPasted" class="button-main-action">TO THE APPLICATION PORTAL</a></p><table style="width: 100%;"><tbody><tr><td style="width: 100%;"><h3>Key Dates</h3><p dir="ltr"><strong>When to apply:&nbsp;</strong>Applications for the award are open. <a href="https://app.bitly.com/Bi6chcjWpnI/links?query=https://wevlv.co/HEMAportal" target="_blank" rel="noopener noreferrer">Visit the application portal</a> to read more and apply.&nbsp;</p><p dir="ltr"><strong>Application closes:</strong> October 31st</p><p dir="ltr"><strong>Award ceremony:</strong> Beginning of 2025</p></td></tr></tbody></table><h2 dir="ltr" id="isPasted">Key technology areas in computational sciences</h2><p dir="ltr">The shift in focus of the award to computational sciences aims to highlight groundbreaking research in fields such as artificial intelligence (AI), quantum computing, and neuro-inspired computing to transform industries, drive innovation, and solve global challenges. By celebrating advances in these areas, the award recognizes work that will enable scientific discovery.</p><h4 dir="ltr">Artificial Intelligence (AI):</h4><p dir="ltr">AI is in a phase of rapid advancement, particularly in machine learning, natural language processing, and computer vision. Potential areas of interest for innovative research include <a href="https://www.wevolver.com/article/shaping-the-future-of-manufacturing-with-llms-and-generative-ai" target="_blank">Smart manufacturing</a>, <a href="https://www.wevolver.com/article/internet-of-things-for-smart-cities" target="_blank">IoT for Smart Cities</a>, and <a href="https://www.wevolver.com/article/smart-grid-navigating-the-future-of-energy" target="_blank">leveraging AI for energy efficiency</a>.</p><h4 dir="ltr">Quantum Computing:</h4><p dir="ltr">Quantum computing has made significant strides, moving from theoretical models to experimental demonstrations of quantum supremacy. Emerging fields of interest for cutting-edge research include <a href="https://www.wevolver.com/article/modular-scalable-hardware-architecture-for-a-quantum-computer" target="_blank">scalable hardware architecture</a>, <a href="https://www.wevolver.com/article/scientists-tune-the-entanglement-structure-in-an-array-of-qubits" target="_blank">entanglement structure of quantum bits</a> and <a href="https://www.wevolver.com/article/breakthroughs-in-quantum-computing" target="_blank">processor developments</a>.&nbsp;</p><p dir="ltr"><strong>Neuro-Inspired Computing:</strong></p><p dir="ltr">Neuro-inspired computing seeks to replicate the efficiency and structure of the human brain in artificial systems. Current research within this area include <a href="https://www.wevolver.com/article/learning-how-to-learn-neuromorphic-ai-inference-at-the-edge" target="_blank">neuromorphic AI at the edge</a>, <a href="https://www.wevolver.com/article/analyzing-human-behavior-with-edge-ai" target="_blank">human behavior analysis</a> and <a href="https://www.wevolver.com/article/advancing-earth-management-through-state-of-the-art-satellite-infrastructure" target="_blank">autonomous robots for remote maintenance</a>.</p><h2 dir="ltr">How to apply:</h2><ol><li dir="ltr"><p dir="ltr"><a href="https://app.bitly.com/Bi6chcjWpnI/links?query=https://wevlv.co/HEMAportal" target="_blank" rel="noopener noreferrer"><strong>Visit the application portal</strong></a> and follow the instructions to create an account and submit your application.</p></li><li dir="ltr"><p dir="ltr"><strong>Application documents:</strong> Documents needed to apply include a recent CV and a clear description of your cutting-edge research and results (no older than 3 years).</p></li><li dir="ltr"><p dir="ltr"><strong>Deadline</strong>: Last day for applications is October 31st.</p></li></ol><p dir="ltr">Visit the application portal to read more and submit your application:</p><p dir="ltr"><a href="https://app.bitly.com/Bi6chcjWpnI/links?query=https://wevlv.co/HEMAportal" target="_blank" rel="noopener noreferrer" id="isPasted" class="button-main-action">TO THE APPLICATION PORTAL</a></p>

Recognizing Groundbreaking Research in AI, Quantum, and Neuro-Inspired Computing

Honoring Innovation in Computational Sciences: Apply for The Heinrich Emanuel Merck Award

<p dir="ltr">The whitepaper titled "Learning how to learn: Neuromorphic AI inference at the edge," published by BrainChip, explores the transformative potential of neuromorphic computing for artificial intelligence (AI) applications, particularly at the edge.&nbsp;</p><p dir="ltr">As traditional computing architectures face limitations, this document outlines how neuromorphic silicon can provide a more efficient, intelligent, and sustainable approach to AI inference.</p><h2 dir="ltr">The Challenges of Conventional Computing</h2><p dir="ltr">The semiconductor industry is at a crossroads. Traditional computing, especially those based on the Von Neumann architecture, is increasingly struggling to keep up with the demands of modern AI applications.</p><p dir="ltr">BrainChip’s whitepaper emphasizes that the current AI paradigm is not sustainable for the next generation of intelligent applications. It outlines several critical issues:</p><ul><li dir="ltr"><p dir="ltr"><strong>Von Neumann Bottleneck: </strong>This refers to the inefficiency arising from separating the memory and processing units in traditional architectures. This leads to delays in data transfer and processing.</p></li><li dir="ltr"><p dir="ltr"><strong>Moore’s Law and Dennard Scaling:&nbsp;</strong>While Moore’s Law has historically predicted the doubling of transistors on a chip every few years, this trend is slowing. Similarly, Dennard Scaling states that as transistors get smaller, the power density remains constant. However, this is no longer true due to limitations like leakage currents and heat dissipation. This limits the performance improvements that can be achieved through conventional means.</p></li><li dir="ltr"><p dir="ltr"><strong>Cloud-Centric Limitations: </strong>AI applications increasingly rely on cloud computing, yet they face challenges such as latency, bandwidth constraints, and security vulnerabilities. The growing reliance on cloud data centers for AI training and inference is not sustainable, especially with the rising demand for real-time processing and the need for privacy in data handling.</p></li></ul><h2 dir="ltr">Neuromorphic Computing: A Paradigm Shift</h2><p dir="ltr">Neuromorphic computing represents a shift in how we approach AI. Unlike traditional models, which rely heavily on centralized processing and extensive data transfers, neuromorphic systems are designed to mimic the human brain's architecture and processing capabilities.&nbsp;</p><p><br></p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1724156635047-1724156635047.png" width="624" height="301" class="fr-fic fr-dii"></p><p dir="ltr">The whitepaper highlights several advantages of this approach:</p><ul><li dir="ltr"><p dir="ltr"><strong>Efficiency:</strong> Neuromorphic chips can process information in parallel, reducing latency and power consumption. This is particularly beneficial for edge applications where resources are limited.</p></li><li dir="ltr"><p dir="ltr"><strong>Event-Driven Processing:</strong> Neuromorphic systems operate on an event-driven basis. This means they only process data when changes occur rather than continuously. This leads to more efficient use of computational resources and lower energy consumption.</p></li><li dir="ltr"><p dir="ltr"><strong>Scalability: </strong>As AI applications evolve, neuromorphic computing offers a scalable solution that can adapt to increasing demands without the same limitations faced by traditional architectures.</p></li></ul><h2 dir="ltr">The Power of "Learning how to Learn"</h2><p dir="ltr">One of the most compelling concepts explored in the whitepaper is "learning how to learn." This idea revolves around the ability of AI systems to go beyond static learning models to embrace a more dynamic, continuous learning process. Traditional AI models are typically trained on a fixed dataset and then deployed.&nbsp;</p><p dir="ltr">However, with time, they don’t adapt to new data unless they are retrained. This process is not only inefficient but also impractical for many real-world applications.</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1724156634645-1724156634645.png" width="624" height="264" class="fr-fic fr-dii"></p><p dir="ltr">Presenting a solution that supports on-device learning, <a href="https://brainchip.com/designing-smarter-and-safer-cars-with-essential-ai-2/" target="_blank">BrainChip’s Akida</a> neuromorphic processor is designed to continue to learn and adapt even after deployment. This capability is crucial for applications like autonomous vehicles, where the environment is constantly changing, and the AI needs to adjust its behavior in real-time.</p><h2 dir="ltr">Real-World Applications and Impact</h2><p dir="ltr">The whitepaper emphasizes the growing importance of edge AI, where data is processed closer to its source rather than being sent to the cloud. This shift is driven by the need for real-time insights and reduced latency. Neuromorphic computing is particularly well-suited for edge applications due to its efficiency and ability to handle complex tasks with limited resources.</p><p dir="ltr">Key applications discussed in the whitepaper include:</p><ul><li dir="ltr"><p dir="ltr"><strong>Autonomous Systems:</strong> Neuromorphic chips can enhance the capabilities of autonomous vehicles and drones by enabling real-time decision-making and processing of sensory data.</p></li></ul><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1724156634797-1724156634797.png" width="624" height="471" class="fr-fic fr-dii"></p><ul><li dir="ltr"><p dir="ltr"><strong>Smart Devices:</strong> From wearable technology to smart home devices, neuromorphic computing can improve performance and battery life. This will make these devices more user-friendly and efficient.</p></li><li dir="ltr"><p dir="ltr"><strong>Healthcare: </strong>In medical applications, neuromorphic systems can facilitate faster diagnosis and monitoring by processing data from various sensors in real time.</p></li></ul><h2 dir="ltr">Towards a Sustainable Future</h2><p dir="ltr">One of the most compelling arguments presented in the whitepaper is the potential for neuromorphic computing to contribute to a more sustainable future. By reducing the energy consumption associated with AI processing, these systems can help mitigate the environmental impact of data centers and cloud computing.</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1724156635656-1724156635656.png" width="624" height="533" class="fr-fic fr-dii"></p><p dir="ltr">The whitepaper posits that as the demand for AI continues to grow, transitioning to neuromorphic architectures will be important to maintain performance while minimizing energy use. This aligns with global efforts to reduce carbon footprints and promote sustainable technology practices.</p><h2 dir="ltr">Final Words&nbsp;</h2><p dir="ltr">"Learning How to Learn: Neuromorphic AI Inference at the Edge" provides an overview of the limitations of traditional computing models and the transformative potential of neuromorphic architectures. By addressing critical challenges such as latency, energy consumption, and security, neuromorphic computing offers a promising path forward for AI applications, particularly at the edge.</p><p dir="ltr">The evolution of computing towards more brain-like architectures not only enhances performance but also paves the way for a more intelligent and sustainable technological future.&nbsp;</p><p dir="ltr">For more insights, read and download the full <a href="https://brainchip.com/wp-content/uploads/2022/08/BrainChip-Learning-how-to-Learn.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">whitepaper here</a>.</p><p dir="ltr"><br></p><p><br></p><p><br></p><p><br></p>

Q&A with Peter Van Der Made, BrainChip Founder and Chief Technology Officer

Learning How to Learn: Neuromorphic AI Inference at the Edge

<p dir="ltr" id="isPasted">Accenture is at the forefront of technological innovation with a focus on developing next-generation computing technologies. In a recent podcast, Dr. Eric Gallo, a senior principal at Accenture Labs, shared insights into the promising field of neuromorphic computing. This technology mimics how the human brain processes information and offers significant advantages in power efficiency and real-time data processing.&nbsp;</p><p dir="ltr">As industries increasingly rely on edge computing, neuromorphic systems present a unique solution to the challenges of energy consumption and integration in smart devices. This episode of the BrainChip podcast explores Accenture's initiatives in neuromorphic computing. They also explore its applications in various sectors and the potential it holds for the future of edge intelligence.</p><h2 dir="ltr">The Rise of Edge Computing and the Need for Heterogeneous Computing</h2><p dir="ltr">Edge computing is rapidly gaining traction as industries increasingly rely on real-time data processing and intelligence at the edge of the network. As the industry matures, there is a growing understanding that a heterogeneous set of computing devices is necessary to achieve optimal results.</p><p dir="ltr">Traditional computing architectures will continue to play a role, but they will make way for other specialized architectures that excel in different situations and locations. This will create a continuum where edge architectures, cloud architectures, and other specialized architectures work together to provide the right amount of compute power where it's needed most.</p><p dir="ltr">Companies with edge devices are actively examining their AI strategies to avoid being left behind. While computing power is readily available, the ability to perform inference very close to the edge is a key focus for many organizations. This is where neuromorphic computing shines, offering a low-power solution for real-time data processing at the edge. This energy efficiency is not just a minor improvement; it’s a game-changer.&nbsp;</p><p dir="ltr">Dr. Gallo highlighted how neuromorphic technology could potentially achieve power savings of up to 100,000 times compared to current methods. This opens up new possibilities for developing intelligent devices that can operate in environments where power is limited, such as in remote locations, wearable devices, or even deep space missions. Neuromorphic computing is still in its early stages, but its potential to transform industries is immense.</p><h2 dir="ltr">The Power of Neuromorphic Technology</h2><p dir="ltr">One of the most significant advantages of neuromorphic technology is its remarkable energy efficiency. Traditional computing systems often require a large amount of power to perform complex tasks. However, neuromorphic systems can achieve the same results while using only a fraction of that energy. This makes them ideal for applications where power is a critical concern.</p><p dir="ltr">Dr. Eric Gallo explained that this technology could be a game-changer in various fields. For example, in defense, neuromorphic chips could be used to create advanced situational awareness systems for soldiers. These systems could process vast amounts of data in real time, helping soldiers make better decisions in the field without the need for bulky, power-hungry equipment.</p><p dir="ltr">Neuromorphic technology could also enhance the intelligence of factory equipment in industrial settings. Machines equipped with neuromorphic chips could adapt to changing conditions on the fly, improving efficiency and reducing downtime. These chips can also be powered by small batteries or energy harvesters, making them suitable for environments where access to power is limited.</p><p dir="ltr">As this technology continues to develop, its impact on different sectors will also grow. This will lead to more efficient and intelligent solutions.</p><h2 dir="ltr">Accenture's Neuromorphic Computing Initiatives in Space</h2><p dir="ltr">Eric Gallo believes that neuromorphic architectures can enable smart satellites and other space devices without the significant power and thermal constraints of traditional computing systems.</p><p dir="ltr">Accenture is working towards demonstrating real-time neuromorphic computing in space. By leveraging the low-power capabilities of neuromorphic chips, Accenture aims to make space devices more intelligent and responsive.</p><p dir="ltr">The space industry has traditionally relied on less advanced computing technologies due to the challenges of power and heat dissipation. However, with the emergence of neuromorphic computing, there is a sudden realization that space systems can be made much smarter without the usual constraints.</p><h2 dir="ltr">Accenture's Partnership with BrainChip</h2><p dir="ltr">Accenture has formed a strong partnership with BrainChip, a leader in neuromorphic computing technology. This collaboration uses the neuromorphic chip to explore practical applications in various industries. Dr. Eric Gallo, who leads Accenture's neuromorphic initiatives, has shared valuable insights from their work with this advanced technology.</p><p dir="ltr">The chip stands out for its ability to save power compared to traditional computing systems. In practical tests, Accenture observed that systems using the chip consumed only a fraction of the power—about one-fifth—compared to conventional setups. This efficiency is important for applications where power resources are limited, such as edge devices and satellites.</p><p dir="ltr">Working with BrainChip has allowed Accenture to access cutting-edge neuromorphic technology and gain practical experience in real-world environments. The partnership has been characterized by strong support and collaboration that enables both teams to tackle challenges and continuously improve their systems.</p><h2 dir="ltr">The Future of Neuromorphic Computing: Spanning Material Spaces and Scales</h2><p dir="ltr">One of the most exciting aspects of neuromorphic computing is its potential to span a wide range of material spaces and scales. Dr. Gallo envisions the possibility of creating tiny, biodegradable neuromorphic sensors that can be used in applications like water quality monitoring. These sensors could transmit data to small neural networks, which could then determine if there is a need for concern.</p><p dir="ltr">At the other end of the spectrum, neuromorphic architectures are enabling large-scale neural networks like Spike GPT. These advanced systems demonstrate the versatility of neuromorphic computing, which can be applied from the smallest sensors to the most powerful artificial intelligence systems.</p><p dir="ltr">Dr. Gallo emphasizes that anyone, even those without expertise in computing architectures, can get involved and contribute to the advancement of neuromorphic computing. The field is open to new ideas and innovations, and there are many opportunities for individuals to make meaningful contributions.</p><h2 dir="ltr">Final Words</h2><p dir="ltr">Looking ahead, the future of neuromorphic computing appears bright. Its ability to span a wide range of materials and scales opens doors for anyone interested in contributing to this exciting field. As organizations like Accenture lead the charge, we can expect to see more practical implementations that harness the power of neuromorphic technology, ultimately making our world smarter and more efficient. To learn more about the future of neuromorphic computing watch the full podcast above!</p>

In this episode of the “This is Our Mission” podcast, Sean Hehir interviews Dr. Eric Gallo, a Senior Principal at Accenture Labs. They discuss the advantages of neuromorphic technology and its impact on edge computing, as well as advancements in SpaceTech. 

BrainChip Podcast Epi 34: The State of Neuromorphic Computing

<p dir="ltr" id="isPasted">Our brains are marvels of efficiency, capable of doing complex tasks while consuming minimal power. But replicating that efficiency with traditional computers has proven difficult. This is where neuromorphic computing comes in. Inspired by the brain's architecture, neuromorphic chips are poised to revolutionize AI, especially at the edge where low-power processing is crucial.</p><p dir="ltr">This episode of the <a href="https://brainchip.com/" target="_blank" rel="noopener noreferrer">BrainChip</a> podcast features a conversation between BrainChip CEO Sean Hehir and Dr. Jason K. Eshraghian, a leading expert in neuromorphic computing. They explore the advantages of this new technology over traditional AI methods and consider its potential to transform the future of Edge AI.</p><h2 dir="ltr">Neuromorphic Approach to AI</h2><p dir="ltr">When asked about the core principles of neuromorphic computing, Dr. Jason K. Eshraghian highlighted some key differences from traditional AI architectures:</p><ul><li dir="ltr"><p dir="ltr"><strong>Brain-Inspired design:</strong> Neuromorphic computing takes inspiration from the human brain," explained Dr. Eshraghian. "Unlike CPUs and GPUs that separate processing and memory, neuromorphic chips integrate both functions, mimicking the brain's structure for improved efficiency.”</p></li><li dir="ltr"><p dir="ltr"><strong>Event-driven computation:&nbsp;</strong>The human brain doesn't waste energy on constantly processing information. Instead, it relies on a system of "spikes" to transmit signals when relevant activity occurs. Neuromorphic chips emulate this approach, only processing data when needed, further reducing power consumption.</p></li><li dir="ltr"><p dir="ltr"><strong>Sparse representations:</strong> The human brain is not uniformly active. Similarly, neuromorphic chips can exploit the inherent sparsity in AI models, focusing on the relevant parts of the data and ignoring the rest. This allows for more efficient use of memory and bandwidth compared to dense representations used in conventional neural networks.</p></li></ul><h2 dir="ltr">Neuromorphic Chips and Edge AI</h2><p dir="ltr">The interview shifted gears when the topic of edge AI came up. Dr. Eshraghian expressed his enthusiasm for how neuromorphic computing aligns perfectly with this growing field:</p><p dir="ltr"><strong>"The advantages of neuromorphic chips become particularly compelling when applied to edge AI applications,"&nbsp;</strong>Dr. Eshraghian explained. "<strong>By ditching the traditional separation of computation and memory, neuromorphic chips achieve significant efficiency gains. This, when combined with their ability only to process data when necessary, makes them ideal for powering intelligent systems directly on devices, closer to the source of the data."</strong></p><p dir="ltr">He elaborated on the benefits of edge AI:</p><ul><li dir="ltr"><p dir="ltr"><strong>Enabling AI at the edge:</strong> The energy efficiency and low latency of neuromorphic architectures make them well-suited for powering AI models directly on edge devices, rather than relying on cloud-based processing. This allows for real-time inference and decision-making without the need to transmit data to remote servers.</p></li><li dir="ltr"><p dir="ltr"><strong>Widespread deployment:&nbsp;</strong>While traditional neural networks running on GPUs can struggle to handle massive numbers of concurrent requests for language models, neuromorphic chips like BrainChip's Akida are designed to process many parallel requests efficiently. This makes them a better fit for large-scale edge deployments.</p></li><li dir="ltr"><p dir="ltr"><strong>Sparse event-driven models:</strong> The sparse, event-driven nature of neuromorphic computing aligns well with language models like BrainChip's TENN. TENN also uses sparse representations and only processes data when necessary. This synergy allows for highly efficient edge AI systems that can handle real-world workloads.</p></li></ul><h2 dir="ltr">BrainChip's Akida and TENN: Hardware Solution Aligned with Neuromorphic Principles</h2><p dir="ltr">The podcast interview discussed BrainChip's Akida, a neuromorphic chip designed with the principles discussed earlier in mind. As Dr. Eshraghian emphasized, the event-driven nature of Akida aligns perfectly with his vision for efficient neuromorphic computing.</p><p dir="ltr">Akida's architecture departs from traditional approaches by relying on an event-based processing system. This means the chip only activates specific neurons when necessary, mimicking the brain's spiking mechanism. This significantly reduces power consumption compared to constantly processing data streams.</p><p dir="ltr">But BrainChip doesn't stop there. The introduction of TENN adds another layer of efficiency. <a href="https://brainchip.com/tenns-a-new-approach-to-streaming-and-sequential-data/" target="_blank">TENN</a> is a state-space model, a type of AI architecture that offers several advantages:</p><ul><li dir="ltr"><p dir="ltr"><strong>Lightweight and efficient:</strong> TENN models require less memory and computational resources compared to traditional deep learning models. This is particularly beneficial for resource-constrained edge devices.</p></li><li dir="ltr"><p dir="ltr"><strong>Fast processing:&nbsp;</strong>TENN models can be processed quickly on neuromorphic chips like Akida, enabling real-time decision-making at the edge.</p></li><li dir="ltr"><p dir="ltr"><strong>Compatibility with Akida:&nbsp;</strong>The design of TENN makes it well-suited for implementation on Akida's architecture, further enhancing the efficiency of the entire system.</p></li></ul><p dir="ltr">By combining Akida's neuromorphic processing power with the lightweight nature of TENN, BrainChip offers a compelling solution for running powerful AI algorithms on edge devices.&nbsp;</p><p dir="ltr">The potential of this approach goes beyond just edge AI. As Dr. Jason K. Eshraghian mentions in the podcast, TENN models can also be used in conjunction with large-scale cloud systems. This creates an interesting combination where lightweight models handle tasks at the edge while complex computations can be offloaded to the cloud when necessary.</p><h2 dir="ltr">Conclusion: A Look Ahead</h2><p dir="ltr">The conversation with Dr. Eshraghian sheds light on the exciting potential of neuromorphic computing. It's not about creating faster, more powerful AI; it's about achieving efficiency and unlocking new possibilities at the edge of networks. While neuromorphic computing is still evolving, it holds the promise to transform various fields, from industrial automation to personal devices.</p>

In this podcast, Sean Hehir, CEO of BrainChip, chats with Dr. Jason K. Eshraghian about neuromorphic computing benefits over traditional AI and its potential to revolutionize the future of computing. Listen in to learn how this emerging technology is shaping the world of AI.


BrainChip Podcast: Neuromorphic Computing Shaping the Future of AI With Dr. Jason K. Eshraghian

<p dir="ltr" id="isPasted">The significance of analyzing human behavior for cutting-edge applications, such as autonomous vehicles, healthcare, and retail, cannot be overstated. In the past, companies relied on centralized cloud servers, which proved to be both costly and introduced latency issues for crucial tasks.</p><p dir="ltr">To address this, businesses have shifted their focus to integrating distributed computing architecture within their infrastructure. <a href="https://www.wevolver.com/article/2024-state-of-edge-ai-report" target="_blank">Edge artificial intelligence</a> is gaining momentum due to its capability to process data near its source, thereby significantly reducing latency and minimizing the need to transmit sensitive information over the internet. This, in turn, reduces the risk of data breaches and enhances privacy.</p><p dir="ltr">Real-time analysis of human behavior has significantly improved decision-making processes. Monitoring driver behavior can greatly enhance road safety. This understanding of human behavior enables companies to develop more intuitive and user-friendly interfaces.&nbsp;</p><p>In response to this need, <a href="https://www.wevolver.com/profile/brainchip" target="_blank">BrainChip</a> and NVISO Group Limited have joined forces to develop an advanced AI-powered system for real-time human behavioral analysis. The system integrates BrainChip's Akida IP and processors, which are based on neuromorphic computing, with NVISO Group's AI software, specializing in analyzing a wide range of human behaviors.</p><p>This article will discuss the core technologies that enable BrainChip’s human behavioral analysis solution to operate efficiently.</p><h2>Brain-Inspired Architecture&nbsp;</h2><p>Neuromorphic systems are engineered to mimic the neural networks of the human brain, with neurons and synapses as their fundamental units. In contrast to the traditional von Neumann architecture, which separates processing and memory units, neuromorphic systems combine these functions.&nbsp;</p><p>BrainChip drew inspiration from this concept to develop its own IP and processor chips based on neuromorphic computing. Thanks to their significant parallelism, these hardware systems can handle many parallel operations, similar to the brain's ability to process multiple tasks simultaneously.</p><p>For human behavioral analysis, the BrainChip Akida IP and processors are capable of processing data in real-time, allowing for immediate analysis and response. This is particularly valuable for applications that require quick decision-making, such as driver monitoring systems (DMS) and interactive consumer electronics.</p><p>The AI-enabled human behavioral analysis solution uses BrainChip’s engineered highly energy-efficient hardware system, making it well-suited for edge deployments. It is designed for integration within resource-constrained environments, and its efficiency is achieved through event-based processing.</p><h2>Why neuromorphic computing?</h2><p>In edge-based applications, generating substantial data can pose significant challenges for cloud processing and global connectivity.&nbsp;</p><p>For its hardware systems, BrainChip has introduced a novel neural network architecture known as Temporal Event-based Neural Networks (TENNs). These lightweight neural networks demonstrate superior efficiency in processing temporal data compared to traditional Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSRM) or Gated Recurrent Units (GRU).</p><p><br></p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dii" style="width: 780px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1718183297364-1718183297364.png" width="624" height="436" class="fr-fic fr-dii"><span class="fr-inner">Akida neural processor architecture [Image Credit: BrainChip]</span></span></span></p><p dir="ltr">The second-generation <a href="https://brainchip.com/akida-generations/" target="_blank">Akida</a> neural processor platform has highly efficient 3-dimensional (3D) convolution functions, enabling it to handle compute-intensive tasks on devices with limited memory and battery resources. This enhancement makes it ideal for ADAS applications.</p><p>The Akida processors support on-chip learning, allowing the system to adapt and learn from new data without needing to connect to the cloud, crucial for analyzing human behavior. The hardware can be integrated into various devices, making them flexible for deployment across multiple sectors, including retail, healthcare, and <a href="https://www.wevolver.com/category/automotive" target="_blank">automotive</a>.&nbsp;</p><h2>The Role of AI Software</h2><p>The jointly developed human behavior analysis solution uses AI software from NVISO Group that can detect and analyze facial expressions, allowing for the determination of emotions. This system monitors users' real-time state by observing head and body pose, eye tracking, and gaze.</p><p>The software processes acquired images through preprocessing to enhance data quality and relevance. It leverages various <a href="https://www.wevolver.com/tag/neural-network" target="_blank">neural networks</a> and machine learning models to extract essential features from the preprocessed images.</p><p>By accurately analyzing and responding to human behaviors, the system enhances the user experience, enabling more seamless and intuitive interactions with intelligent devices. Its continuous learning and adaptation to user behaviors ensure personalized and context-aware responses.</p><p>NVISO has created an evaluation kit for its human behavior AI SDK, which runs on the BrainChip Akida neuromorphic processing platform. “In our goal to drive machines to understand people and their behaviors, we have partnered with BrainChip to develop a high-performance system that enables efficient and effective human interaction with intelligent systems,” says Virpi Pennanen, CEO of NVISO Group<sup>[2]</sup>.</p><h2>Conclusion</h2><p>The integration of AI human behavioral software with Akida neuromorphic computing has led to the development of a system that empowers companies to build intelligent systems. With the increasing presence of autonomous machines, these systems can interact with humans by identifying and interpreting movement using the capabilities of artificial intelligence.</p><p>This co-developed human behavioral analysis solution was showcased at the CES 2024 and IFS Direct Connect 2024<sup>[1,2]</sup>.</p><h2>References</h2><p>[1] BrainChip. BrainChip Demonstrates Human Behavior Detection at IFS Direct Connect 2024. February 15, 2024. <a href="https://brainchip.com/brainchip-demonstrates-human-behavior-detection-at-ifs-direct-connect-2024/" target="_blank">https://brainchip.com/brainchip-demonstrates-human-behavior-detection-at-ifs-direct-connect-2024/</a>. Accessed on May 28, 2024.&nbsp;</p><p>[2] BrainChip. BrainChip and NVISO Group Demonstrate AI-Enabled Human Behavioral Analysis at CES 2024. January 5, 2024. <a href="https://brainchip.com/brainchip-and-nviso-group-demonstrate-ai-enabled-human-behavioral-analysis-at-ces-2024/" target="_blank">https://brainchip.com/brainchip-and-nviso-group-demonstrate-ai-enabled-human-behavioral-analysis-at-ces-2024/</a>. Accessed on May 28, 2024.&nbsp;</p>

Integration of AI human behavioral software with Akida neuromorphic computing can drive machines to understand human behavior.

Analyzing Human Behavior with Edge AI

<p dir="ltr" id="isPasted">Edge AI leader BrainChip recently held its quarterly investor podcast, addressing key developments and answering burning questions from shareholders. Artificial intelligence (AI) is rapidly evolving, with a growing focus on processing power at the "edge" of networks. This means less reliance on centralized cloud computing and more emphasis on on-device intelligence.&nbsp;</p><p dir="ltr">In this article, we dive into the highlights from the podcast, covering BrainChip's successful conference participation, exciting space deployment, and its approach to securing commercial partnerships.</p><h2 dir="ltr">Insights from the Embedded World Conference</h2><p dir="ltr">The Embedded World Conference in Frankfurt, Germany, served as a crucial platform for BrainChip to showcase its innovative AI solutions and engage with industry leaders. CEO Sean Hehir's participation at the conference showed a landscape buzzing with discussions on AI at the edge, a theme resonating strongly throughout the event.&nbsp;</p><p dir="ltr">BrainChip's presence at the conference was met with success, evident from the tremendous booth traffic and the strategic positioning within the tiny ML Pavilion. Noteworthy demonstrations, including an emotion detection offering in collaboration with Inviso and the VVDN Edge Boox, garnered significant interest from investors, existing clients, and potential prospects.&nbsp;</p><p dir="ltr">The conference provided a valuable networking opportunity and set the stage for promising collaborations and future growth opportunities for Brain Chip in the dynamic field of AI technology.</p><h3 dir="ltr">AI Technology in Space</h3><p dir="ltr">A landmark moment for BrainChip occurred on March 5th, 2024, when their AI technology, Akida, was launched into space aboard the ANT-61 spacecraft. While a formal ASX announcement wasn't made due to specific criteria, the launch represents a significant validation of Akida's capabilities in harsh environments where low power consumption and offline processing are crucial.&nbsp;</p><p dir="ltr">This event represents a significant milestone, showcasing BrainChip’s advancements in neuromorphic computing and highlighting the robustness of their technology in extreme environments.</p><p dir="ltr">Despite the magnitude of this achievement, some shareholders questioned the absence of an ASX announcement to commemorate this historic event. In response, CEO Sean Hehir clarified that while the company adheres to ASX guidelines, the space launch did not meet the criteria for a standalone announcement.&nbsp;</p><p dir="ltr">Emphasizing the importance of this venture, Hehir highlighted BrainChip's neuromorphic technology's unique capabilities in space environments, as evidenced by a blog from <a href="https://brainchip.com/neuromorphic-computing-making-space-smart/" target="_blank">Accenture</a> and a press release from the <a href="https://brainchip.com/european-space-agency-talks-spacetech-challenges-and-opportunities/" target="_blank">European Space Agency</a>.&nbsp;</p><h2 dir="ltr">Remuneration Report and Commercial Engagements</h2><p dir="ltr">The 2023 Annual General Meeting (AGM) saw BrainChip receive a first strike against its remuneration report, prompting the company to make substantial changes to its policies to address the concerns raised by proxy advisors and institutional investors.&nbsp;</p><p dir="ltr">CEO Sean Hehir acknowledged the possibility of a second strike at the upcoming 2024 AGM. He stated that the company has made prudent preparations to handle such a scenario if it were to occur effectively. However, Hehir expressed optimism that the significant improvements made to the remuneration report would prevent a second strike.</p><h2 dir="ltr">Akida Edge AI Box on Track: Pre-Orders, Partnerships, and Sales Strategy</h2><p dir="ltr">Investors eager for updates on the Akida Edge AI Box won't be disappointed. The podcast provided valuable insights into this exciting product:</p><h3 dir="ltr">1. Pre-Orders Progressing Well</h3><p dir="ltr">News regarding pre-orders for the Akida Edge AI Box was positive. While specific figures weren't disclosed, CEO Hehir confirmed that pre-orders are in line with expectations. This indicates strong initial interest from potential customers.</p><h3 dir="ltr">2. Partnering for Success</h3><p dir="ltr">BrainChip clarified its sales strategy for the Akida Edge AI Box. The primary sales channel for larger-volume purchases is through their established partner, VVDN. For smaller quantities, BrainChip's website handles pre-orders. It also gathers valuable customer feedback and identifies potential use cases. This two-pronged approach ensures efficient sales while providing useful insights for product development.</p><h3 dir="ltr">3. Securing Commercial Contracts</h3><p dir="ltr">A central focus for BrainChip is securing commercial contracts. CEO Hehir expressed confidence in BrainChip's competitive edge. The company hasn't lost to competitors yet and is engaged in deep discussions with potential clients.&nbsp;</p><h2 dir="ltr">Engagement with Institutional Investors and Proxy Advisors</h2><p dir="ltr">During the first quarter of 2024, BrainChip's CEO, Sean Hehir, engaged in a series of crucial meetings with institutional investors and proxy advisors in Sydney and Melbourne. These meetings played a significant role in shaping the company's strategic direction and investor relations.&nbsp;</p><p dir="ltr">Proxy advisors, in particular, are essential in influencing voting outcomes at the Annual General Meeting (AGM) by guiding institutional investors on critical resolutions related to corporate governance, remuneration, and ESG matters.&nbsp;</p><p dir="ltr">By actively engaging with proxy advisors, BrainChip aims to gain valuable insights into investor sentiments and expectations, ensuring alignment with best practices and transparency in corporate governance.</p><h2 dir="ltr">Conclusion: A Look Ahead for BrainChip</h2><p dir="ltr">The BrainChip quarterly investor podcast provided a comprehensive overview of the company's landscape and vision. Overall, the podcast paints a positive picture of BrainChip's path forward. By capitalizing on market opportunities, attracting top talent, and fostering strong investor relations, BrainChip appears well-positioned to become a leader in the burgeoning edge AI revolution.&nbsp;</p><p dir="ltr">Listen to the full podcast&nbsp;<a href="https://www.youtube.com/watch?v=UTRkO9DFtn4&embeds_referring_euri=https%3A%2F%2Fbrainchip.com%2Finvestor-podcast-ep-5-insights-into-brainchips-latest-developments%2F&source_ve_path=OTY3MTQ&feature=emb_imp_woyt" target="_blank" rel="noopener noreferrer" id="isPasted">here.</a></p><p dir="ltr">Investors interested in learning more about BrainChip's exciting developments are encouraged to visit the company's website at <a href="https://brainchip.com/" target="_blank">https://brainchip.com/</a>.&nbsp;</p>

Explore BrainChip's latest advancements by tuning into Episode 5 of their Quarterly Investor Podcast. CEO Sean Hehir addresses critical topics for shareholders, including the successful Akida space launch, takeaways from the Embedded World Conference, and updates on the Akida Edge AI Box. 

Investor Podcast Ep 5: Insights into BrainChip's Latest Developments

<p dir="ltr" id="isPasted">Continual advancements in space technology are reshaping our capacity to manage Earth's affairs with precision and efficiency. Amidst the complexities of urbanization, climate change, and global connectivity, the need for sophisticated infrastructure has become increasingly apparent.&nbsp;</p><p dir="ltr">Satellites, with advanced functionalities, have emerged as indispensable tools for data acquisition, analysis, and communication, which are crucial for informed decision-making on Earth.</p><p dir="ltr">With their high-resolution cameras, multispectral sensors, and robust communication capabilities, we can capture and transmit data from a wide range of Earth-related activities, from urban planning and disaster management to agricultural monitoring and environmental protection.</p><p dir="ltr">By continuously orbiting the Earth, satellites capture detailed images and other sensor data covering the whole planet’s surface. We can use this data to track changes in landscapes, monitor weather patterns, and observe natural disasters like forest fires, floods, and hurricanes in real time.</p><p dir="ltr">This data is collected and transmitted back to Earth, where it can undergo rigorous analysis. Machine learning algorithms, data analytics, and other advanced computing techniques allow us to interpret the data with ease. For example, predictive modeling is utilized to forecast weather changes or track the migration patterns of endangered species. The ability to process real-time data enables quick decision-making, which is especially crucial during responses to natural disasters and emergencies.&nbsp;</p><p dir="ltr">Communication is another critical functionality of satellites. They ensure communication lines are able to remain open, even in the most remote parts of the world. This facilitates global connectivity, allowing data, voice, and video communication to be transmitted across diverse geographical landscapes.</p><h1 dir="ltr">Maintenance in space</h1><p dir="ltr">On Earth, if something breaks down in a factory or a power plant, we don’t build a replacement. Instead, we send a repair team with spare parts, and even better yet, we monitor the components' wear and replace them before they break down. This preventive maintenance approach minimizes downtime and reduces the costs associated with sudden breakdowns.</p><p dir="ltr">This methodology is critical in industries where reliability and uptime are essential. By continuously monitoring equipment health through sensors and predictive analytics, we can identify potential problems before they lead to operational failures. However, this approach faces unique challenges in space, where sending a repair team is not feasible due to the vast distances and extreme conditions.</p><p dir="ltr">The transition from Earth-based maintenance to space requires innovative solutions to overcome these challenges. The harsh space environment, including vacuum conditions, microgravity, and cosmic radiation, makes traditional maintenance methods impractical. Additionally, the delay in communication between Earth and spacecraft further complicates real-time interventions, necessitating a more autonomous approach to maintenance and repair operations in space.</p><p dir="ltr"><a href="https://www.ant61.com/" target="_blank" rel="noopener noreferrer">ANT61&nbsp;</a>develops autonomous robots that enable the maintenance of satellites without the need to put human lives at risk. ANT61 is providing solutions for both: their Beacon product allows satellite operators to understand what went wrong with their satellites and restore them back to operation.</p><p dir="ltr">For larger and more expensive satellites, they are building robots that will dock, refuel, and, in the future, refurbish satellites, prolonging their useful life. At the core of these robots lies ANT61 BrainTM, the innovative devices that combine machine vision and decision-making technology, enabling the autonomy of these maintenance robots. Autonomy is very important as, due to the speed of light limitations, it won’t be possible to control every movement of these robots remotely from Earth.</p><h3 dir="ltr">BrainChip AkidaTM&nbsp;: A revolutionary neuromorphic processor for edge AI applications</h3><p dir="ltr">The first generation of the ANT61 Brain uses the <a href="https://brainchip.com/akida-neural-processor-soc/" target="_blank" rel="noopener noreferrer">BrainChip Akida<sup>TM</sup></a> chip for power-efficient AI and is currently on board the Optimus-1 satellite, which was deployed recently by the SpaceX Transporter-10 mission. ANT61 will test the ANT61 Brain later this year and perform training and evaluation of various neural networks that we will use for future in-orbit servicing technology demonstrations.</p><p dir="ltr">The BrainChip Akida™ chip is a groundbreaking advancement in neuromorphic computing, designed to mimic the neural architecture of the human brain. This technology stands out for its ability to process data directly at the point of acquisition, greatly enhancing efficiency by reducing the reliance on distant cloud-based systems for data processing.</p><p dir="ltr">Akida's unique capability lies in its event-based neural processor, which processes data only when needed, using data sparsity to significantly reduce the number of operations required. This leads to lower power consumption and heat dissipation, making it highly suitable for the demanding conditions of space. Each Akida node consists of four Neural Network Layer Engines (NPEs), which can be configured for various functions, such as convolutional layers or fully connected layers, and support incremental learning and high-speed inference. This configuration flexibility allows the chip to efficiently handle a wide range of AI tasks without needing constant connectivity or high power draw.</p><p dir="ltr">Akida's scalable architecture enables the chip to support up to 256 nodes in a mesh network, boosting computational power while maintaining a small footprint. This scalability is crucial for space applications where efficiency and performance are vital. The entire neural network can be embedded directly within the Akida fabric, eliminating the need for frequent data transfers that consume significant power and reduce system responsiveness.</p><p dir="ltr">Another key feature of the Akida chip is its on-chip learning capability, which allows it to adapt and improve its performance over time without external intervention. This feature is particularly beneficial in space, where conditions can change rapidly and autonomy is crucial. By processing and learning from data locally, Akida enhances the autonomy of satellite maintenance robots like those developed by ANT61, enabling them to perform complex tasks with minimal latency and enhanced reliability.</p><p dir="ltr">The ANT61 team chose to partner with BrainChip because they believe that neuromorphic technology will bring the same exponential improvement in AI as 20 years ago. The shift from CPU to GPU opened doors to deep neural network applications, which are at the core of all AI technologies today. Neuromorphic technology is also perfect for space: the lower power consumption means less heat dissipation, and we can get up to five times more computational power for the same electrical power budget.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 790px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1715339791069-Akida_1000_chip-1.jpg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Akida AKD1000 is a reference chip implemented with TSMC at 28nm</span></span></span></p><h1 dir="ltr">The ANT61 vision for the future</h1><p dir="ltr">Humanity’s expansion to the cosmos requires infrastructure that can only be built by robots. With its in-orbit servicing experience, ANT61 aims to become the main supplier of the robotic workforce for Moon and Mars installations. This will enable companies from Earth to project their ambition to space, providing valuable services and resources for future off-world factories and cities.</p><p dir="ltr">Neuromorphic computing, exemplified by technologies like the BrainChip Akida™ chip, is set to play a pivotal role in this spacefaring era. This form of computing brings several advantages crucial for space applications, such as low power consumption, high efficiency, and the ability to perform complex computations needed for autonomy. By mimicking the human brain, neuromorphic processors can process large volumes of sensor data efficiently, making real-time decisions without the need to send data back to Earth for processing.</p><p dir="ltr">The integration of neuromorphic computing into space robotics promises to significantly improve these machines' capabilities. Robots equipped with advanced AI processors can better handle the unpredictable nature of space environments, adapting to new challenges autonomously and performing tasks ranging from construction to maintenance and repair. This capability ensures that space habitats are built efficiently and can be sustained with minimal human oversight.</p><p dir="ltr">ANT61's vision of utilizing these technologies to support human expansion into space reflects a forward-thinking approach that will not only facilitate the construction of off-world establishments but also ensure their long-term sustainability and success.&nbsp;</p>

Remote satellite maintenance is crucial to critical data acquisition and global communication


Advancing Earth Management Through Neuromorphic Computing Powered Satellites

<p>Space exploration has captivated humanity for decades with its promise of adventure and scientific discovery. But venturing beyond our atmosphere comes with immense challenges. This is where a new wave of technology is poised to revolutionize the space industry: artificial intelligence (AI) and robotics.&nbsp;</p><p>This episode of the BrainChip podcast is a conversation between BrainChip CTO Dr Tony Lewis and Mikhail Asavkin, founder of Australian space tech company ANT61. They talk about how AI and intelligent robots are no longer science fiction but the key to unlocking the future of space exploration.&nbsp;</p><h2 dir="ltr">Reusable Rockets and the Democratization of Space</h2><p dir="ltr">Over the past decade, we have witnessed a remarkable transformation propelled by innovation and the rise of private industry in the space sector. The ability to recover and relaunch rockets has dramatically reduced the cost of space access, opening the door for a new era of private space exploration companies.&nbsp;</p><p>This democratization of space has spurred innovation and ignited a global race to push the boundaries of what's possible. Mikhail Asavkin, drawing from his extensive experience in space engineering, sheds light on this evolutionary journey and explains how companies like ANT61 are now able to focus their resources on cutting-edge technologies like AI and robotics, thanks to the more affordable launch options provided by reusable rockets.&nbsp;</p><p>This shift has fundamentally changed the landscape of space exploration, making it a more accessible and dynamic field.</p><h2 dir="ltr">The Impact of AI on Space Exploration</h2><p dir="ltr">Mikhail Asavkin and Dr Tony Lewis also discuss the transformative role of AI in space exploration. Here's why AI is the silent hero propelling us further into the cosmos:</p><ul><li dir="ltr"><p dir="ltr">Due to the speed of light, communicating with robots on the Moon or Mars in real time is impossible. This is where AI steps in. By enabling robots to make autonomous decisions based on sensor data and their surroundings, AI eliminates the need for constant human intervention from Earth.</p></li><li dir="ltr"><p dir="ltr">Space exploration demands more than just following pre-programmed instructions. Robots must be able to perceive their environment, identify objects, and navigate complex situations. Advancements in AI, particularly image recognition and deep learning, allow robots to "see" and understand their surroundings, enabling them to perform intricate tasks and make critical decisions independently.</p></li></ul><h2 dir="ltr">Neuromorphic Computing in Space</h2><p dir="ltr">The limitations of traditional AI systems, particularly their high power consumption, pose a significant challenge for space exploration. Here's where neuromorphic computing enables new ways of exploring space.&nbsp;</p><p>Inspired by the structure and function of the human brain, neuromorphic chips are designed to process information similarly. This translates to a dramatic reduction in power consumption compared to traditional AI hardware like GPUs (Graphics Processing Units). For spacefaring robots, this is a game-changer.</p><p>Neuromorphic technology offers a multitude of benefits for space AI:</p><ul><li dir="ltr"><p dir="ltr">Neuromorphic chips enable this dream by requiring significantly less power to perform complex tasks like object detection and real-time decision-making.</p></li><li dir="ltr"><p dir="ltr">Neuromorphic chips excel at real-time processing, allowing robots to make critical decisions without delay. This is essential for tasks like navigating hazardous terrain or performing delicate scientific operations.</p></li></ul><h2 dir="ltr">Training AI on the Go</h2><p dir="ltr">Traditionally, training AI models involves massive datasets downloaded from Earth and meticulously labeled by humans – a time-consuming and impractical process in the vast expanse of space.</p><p>Neuromorphic computers, with their ability to learn and adapt in real time, make this possible. The AI model can continuously improve its capabilities and decision-making abilities by processing sensor data and surroundings. By training on-site, the AI model can adapt to specific situations and environments, making its tasks more robust and efficient.</p><p>However, the podcast also discusses a significant challenge that remains: ensuring the safety and well-being of human explorers venturing into the unknown. This necessitates further advancements in AI, particularly in areas like risk assessment, anomaly detection, and real-time decision-making.</p><h2 dir="ltr">Conclusion: New Era in Space Exploration</h2><p dir="ltr">From enabling autonomous robots to construct lunar habitats to empowering AI models to learn and adapt on the fly, these technologies are revolutionizing how we venture beyond our planet. As we look towards a future where humanity establishes a presence on other celestial bodies, AI and robotics will be our indispensable companions.</p><p dir="ltr">Listen to the full podcast <a href="https://www.youtube.com/watch?v=yyVWVjYM-nQ&t=3s" target="_blank" rel="noopener noreferrer">here.</a></p>

In this episode, ANT61 Founder and CEO Mikhail Asavkin joins BrainChip CTO Dr. Tony Lewis to discuss the evolution of Space Technology and its impact on daily life. 

BrainChip Podcast: Robotics in Space discussion with ANT61 Founder Mikhail Asavkin

<p dir="ltr" id="isPasted">Have you ever wondered how the next generation of space exploration would be powered? The answer might lie in artificial intelligence (AI), and a new frontier is emerging: AI in space.&nbsp;</p><p dir="ltr">This episode of the Brain Chip podcast explores the vital role of BrainChip in pushing AI to new frontiers through Akida, which was recently launched into space aboard the Transporter 10 spacecraft, alongside Dr. Tony Lewis, BrainChip’s CTO, and experts from the European Space Agency (ESA) Laurent Hili and Luis Mansilla.</p><h2 dir="ltr">The Rise of New Space&nbsp;</h2><p dir="ltr">The podcast begins by highlighting the dramatic shift in space exploration. Gone are the days of solely government-driven endeavors. Private companies like SpaceX are making space more accessible, leading to a new era of "New Space." This increased activity necessitates advancements in spacecraft autonomy and mission efficiency.</p><p dir="ltr">One solution gaining traction is AI. Implementing AI systems directly on spacecraft can automate complex tasks, freeing human astronauts for critical decision-making and exploration activities. Additionally, onboard AI can analyze data in real time, enabling faster responses to unforeseen situations and optimizing mission parameters. However, the podcast emphasizes the challenges of using traditional AI hardware in space.</p><h2 dir="ltr">Neuromorphic Computing: A Promising Path Forward</h2><p dir="ltr">The podcast also explores the complexities of implementing AI in space. One of the most significant hurdles is power consumption. Traditional AI hardware can be power-hungry, a major constraint for spacecraft with limited energy resources. As discussed, traditional computing struggles with the power constraints of space.&nbsp;</p><p dir="ltr">This is where neuromorphic computing emerges as a game-changer. Unlike conventional processors that rely on the von Neumann architecture, neuromorphic computing takes inspiration from the human brain.</p><p dir="ltr">Here's how neuromorphic computing breaks the mold:</p><ul><li dir="ltr"><p dir="ltr">Mimic the brain's structure for parallel processing and potential efficiency gains.</p></li><li dir="ltr"><p dir="ltr">Consume significantly less power, which is crucial for space where every watt counts.</p></li><li dir="ltr"><p dir="ltr">Can be tailored for specific AI tasks, further boosting efficiency.</p></li></ul><p dir="ltr">While still young, neuromorphic computing holds immense promise for space AI.</p><p dir="ltr">This is where AI comes in. Artificial intelligence has the potential to revolutionize space exploration by enabling:</p><ul><li dir="ltr"><p dir="ltr">Onboard decision-making: AI algorithms could analyze sensor data and make real-time decisions without relying on constant communication with Earth, which vast distances and time delays can hinder.</p></li><li dir="ltr"><p dir="ltr">Autonomous operations: Spacecraft equipped with AI could perform tasks like navigation, anomaly detection, and even scientific data analysis with minimal human intervention.</p></li><li dir="ltr"><p dir="ltr">Data reduction and filtering: AI can sift through vast amounts of data collected by space instruments, identifying valuable information and reducing the amount that needs to be transmitted back to Earth, saving precious bandwidth.</p></li></ul><h2 dir="ltr">ESA's Multifaceted Approach to AI in Space</h2><p dir="ltr">The podcast goes beyond just neuromorphic computing, showcasing the multifaceted approach of the European Space Agency (ESA) to integrating AI into various space missions. As highlighted by ESA, they are exploring AI applications across different departments, including:</p><ul><li dir="ltr"><p dir="ltr">Beamforming and anti-jamming technologies for improved communication and signal strength.</p></li><li dir="ltr"><p dir="ltr">Maritime applications such as ice bear detection for environmental monitoring.</p></li><li dir="ltr"><p dir="ltr">Earth observation tasks like analyzing data from the new SC of the Cloud.</p></li></ul><p dir="ltr">They also discussed ESA's adoption of the NASA Technology Readiness Level (TRL) system to assess the maturity of AI technologies for space use. This standardized approach helps prioritize development efforts and ensures that technologies are adequately tested before flight.</p><h2 dir="ltr">BrainChip's AK100</h2><p dir="ltr">The podcast discusses BrainChip's AK100 chip and its potential role in ESA's space missions. BrainChip reveals that the AK100 chip is currently at TRL 3, indicating it's in the early stages of development. Further work is required to harden the technology for space environments, including rigorous testing for radiation resistance and mitigation strategies.</p><h2 dir="ltr">Conclusion: A Collaborative Future for AI in Space</h2><p dir="ltr">The BrainChip podcast episode offered a glimpse into the exciting and complex world of AI in space. While challenges remain, particularly regarding power consumption and technology validation, the potential benefits of AI for space exploration are undeniable. The collaboration between ESA and BrainChip exemplifies the innovative spirit driving this new frontier.</p><p><br></p>

Listen in as Dr. Tony Lewis, BrainChip's CTO, Laurent Hili (Microelectronics Engineer), and Luis Mansilla (AI Expert) discuss the growing role of AI in space exploration, ESA's investments in this technology, and how BrainChip's Akida chip contributes to this exciting frontier

BrainChip Podcast: Insights on SpaceTech With European Space Agency ESA.

<p id="isPasted">The <a href="https://brainchip.com/podcast/" target="_blank" rel="noopener noreferrer">BrainChip podcast</a> is a valuable resource for engineers interested in the future of neuromorphic computing. In their recent episode, BrainChip's Rob Telson, spoke with Edge Impulse's CTO, Jan Jongboom, about machine learning on the edge.</p><p><strong>The Powerhouse: Edge Impulse</strong></p><p>Before diving into the episode's content, it's crucial to understand the prowess of Edge Impulse. Edge Impulse empowers developers to craft intelligent device solutions harnessing embedded Machine Learning (ML). With a focus on real-world applications, their platform streamlines the process of gathering sensor data, cloud-based ML model training, and deploying those models to edge devices. This has opened doors to myriad applications, from predictive maintenance and asset tracking to health monitoring, making it a game-changer in the IoT landscape.</p><h2>A conversation between two industy leaders</h2><p>The conversation between Rob and Jan covers a wide range of ideas. If you're an engineer or a tech aficionado, this episode is a goldmine of insights into a rapdily evolving industry. It's not just about understanding the technologies but visualizing their real-world applications. Imagine a world where devices self-optimize, predict failures before they occur, and adapt to user behaviors in real-time. That's the future BrainChip and Edge Impulse are paving the way for. Topics covered inlcude:</p><ul><li><p><strong>Neuromorphic Computing:</strong> This technology, inspired by the human brain's neural structures, is the future of computing. Offering unparalleled speed, efficiency, and adaptability, neuromorphic computing is set to revolutionize the tech world.</p></li><li><p><strong>Machine Learning on the Edge:</strong> The synergy of machine learning and neuromorphic computing is the episode's highlight. Jan and Rob elucidate how this fusion can birth smarter IoT devices capable of rapid, efficient information processing without relying on cloud computations.</p></li><li><p><strong>Redefining IoT:</strong> The transformative potential of neuromorphic computing in the IoT domain is undeniable. Devices are not just becoming smarter; they're evolving to intuitively cater to user needs, optimize energy use, and bolster security.</p></li></ul><p><strong>Concluding Thoughts</strong></p><p>The fusion of machine learning with neuromorphic computing is not just a technological advancement; it's a paradigm shift. As we inch closer to a hyper-connected future, this episode offers a lens into the innovations and challenges on the horizon. For those on the fence about tuning in, consider this: knowledge is power, and this podcast episode promises to supercharge your understanding of the next big thing in IoT.</p>

This podcast episode promises to supercharge your understanding of the next big thing in IoT.

BrainChip Podcast: Deep Dive into the Impact of Neuromorphic Computing on IoT

<h2 dir="ltr" id="isPasted">What is Akida Edge?</h2><p dir="ltr">The Akida neuromorphic processor from <a href="https://brainchip.com/" target="_blank" rel="noopener noreferrer">BrainChip</a> is a third-generation neural network designed to accelerate advanced AI at the edge. It is created after researching the neuronal architecture of the human brain. The Akida processor aims to accelerate neural networks such as recurrent neural networks (RNNs), deep neural networks (DNNs), convolutional neural networks (CNNs), and vision transformers (ViTs) in hardware.</p><p dir="ltr">It also supports Temporal Event-based Neural Nets (TENNs), a unique neural network architecture designed for quick analysis of complex time-series data.</p><p dir="ltr"><a href="https://www.youtube.com/watch?v=6CH8tx-KQkI" target="_blank" rel="noopener noreferrer">In their recently released video</a>, BrainChip demonstrates how the Akida can be applied.&nbsp;</p><h2 dir="ltr">Cutting Edge Applications of Akida Edge</h2><h3 dir="ltr">1. Time-of-Flight (ToF) Anti-Spoofing</h3><p dir="ltr">Time of Flight (ToF) anti-spoofing is a technology used to enhance security systems by accurately detecting and preventing spoofing attempts. One notable application of ToF anti-spoofing is in facial recognition systems, where the technology can differentiate between a real person’s face and a spoofed image or mask to ensure secure access control.&nbsp;</p><p dir="ltr">This feature is demonstrated in the BrainChip demo reel by showcasing Akida Edge learning with ToF sensors. The system accurately identifies the man named Rob. However, when Rob attempts to deceive the system by holding an image in front of his face, the system initially categorizes it as 'unknown.' This demonstration illustrates how Akida can discern between a genuine face and a spoofed image or mask.</p><h3 dir="ltr">2. Keyword Spotting</h3><p dir="ltr">Keyword spotting is a speech recognition approach for identifying individual words or phrases in audio. Keywords are provided to the system that trigger an action. For example, a security system may be trained to listen to the sound of breaking glass and set off an alarm upon detection.&nbsp;</p><p dir="ltr">In the BrainChip demo reel, this feature is demonstrated by a drone. When the man says 'on,' the drone begins functioning. It ascends with the sound 'up,' and descends with the keyword 'off.'&nbsp;</p><h3 dir="ltr">3. Vibration Analysis&nbsp;</h3><p dir="ltr">Vibration Analysis is a diagnostic technique for detecting, tracking, and preventing malfunctions in revolving and rotating machinery. It involves the assessment of vibration patterns to identify deviations indicative of potential issues.&nbsp;</p><p dir="ltr">For instance, it is employed in monitoring the performance of turbines, motors, pumps, and other rotating equipment in various sectors, including manufacturing, energy, and transportation.</p><p dir="ltr">In the BrainChip demo reel, this feature is demonstrated by a fan. When the fan is activated, Akida accurately predicts the normal vibration pattern as ‘on.’ However, when an external factor, such as inserting a paper into the fan, causes vibrations to become distorted, Akida promptly detects this anomaly and predicts a defect.&nbsp;</p><h3 dir="ltr">4. Facial Recognition</h3><p dir="ltr">Facial recognition is a biometric technology used to identify and verify individuals by analyzing facial features. It is used across various sectors, including security, access control, and personalized user experiences in consumer electronics.</p><p dir="ltr">In the BrainChip demo reel, a recognition system powered by the iToF AF0130 Devkit + Akida IP, utilizing indirect Time-of-Flight (iToF) technology, is showcased.&nbsp;</p><p dir="ltr">Initially identifying individuals as "Unknown," the system quickly learns to recognize them upon inputting their names. Notably, individuals named Nolan and Chris are accurately identified after their names are entered. This demonstration highlights the system's adaptability and learning capabilities.</p><h3 dir="ltr">5. Beer Tasting with Akida</h3><p dir="ltr">Beer tasting allows you to discern the origins, composition, style, ingredients, and overall characteristics of specific drinks. Beer tasting is crucial in brewery quality control, consumer taste testing, and beverage industry research and development.&nbsp;</p><p dir="ltr">In the BrainChip demo reel, different beers are ‘tasted’ using Akida. Each beer is analyzed by Akida, which produces classification results based on the predominant taste ingredient. Initially, a sample is tested, revealing pale ale as the top taste, followed by other classifications.&nbsp;</p><p dir="ltr">Subsequently, another sample is tested, with stout identified as the winning taste, prompting Akida to announce, 'I tasted Stout.' This showcases Akida's ability to accurately classify beer tastes.</p><h3 dir="ltr">6. In-cabin Visual Wake</h3><p dir="ltr">In-cabin visual wake is a sensor-based technology integrated with Akida AI that detects human presence and identifies individuals entering or exiting a cabin or enclosed space. You may use the in-cabin feature to make your home ‘smart’ by providing proactive security alerts, intuitive voice commands for smart device management, and detecting the presence of any approaching person for several tasks.</p><p dir="ltr">In the BrainChip demo reel, the in-cabin visual wake option powered by Akida AI sensor technology is showcased. Using an automobile as an example, the system demonstrates its ability to detect human presence and recognize individuals entering or exiting the car. For instance, when Aaron enters the car, the system identifies his presence.&nbsp;</p><p dir="ltr">Furthermore, the system demonstrates simultaneous functions:</p><ul><li dir="ltr"><p dir="ltr">Akida Keyword Spotting:&nbsp;Aaron approaches and triggers the system by saying "Hey Akida," to which Akida responds.</p></li><li dir="ltr"><p dir="ltr">Akida Face Recognition:&nbsp;Akida recognized the individual and said, "Hey Aaron," just as he sat in the car.</p></li><li dir="ltr"><p dir="ltr">Akida Visual Wake Words:&nbsp;Following Aaron's interaction, when Tod opens the car door, Akida detects his presence.</p></li></ul><h3 dir="ltr">7. Regression Analysis</h3><p dir="ltr">Regression Analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It can be used in civil engineering and infrastructure maintenance to predict structural issues in buildings, bridges, etc.&nbsp;</p><p dir="ltr">In the BrainChip demo reel, Akida is showcased for regression analysis to monitor the structural health of a bridge. The demonstration illustrates how Akida predicts regression as ‘normal’ during routine operations. However, anomalies detected by Akida signal potential structural issues, prompting an ‘ALERT’ prediction.</p><h3 dir="ltr">8. Gesture Recognition</h3><p dir="ltr">It is a user interface technology that uses cameras or sensors to capture and interpret human gestures, translating them into actionable data. It is used for intuitive interaction with digital devices and virtual objects.</p><p dir="ltr">In the BrainChip demo reel, gesture recognition is showcased as a compelling application of Akida technology. The video demonstrates how Akida interprets and responds to different hand movements captured by Time-of-Flight (ToF) sensors. For instance, the demonstration features a man performing various hand gestures, ranging from a hand clap to playing air guitar, which Akida associates with percentage values.</p><p dir="ltr"><a href="https://www.youtube.com/watch?v=6CH8tx-KQkI" target="_blank">Watch the full video</a> to understand better the applications of neuromorphic technology.</p>

The latest advancements in Akida Edge technology have showcased remarkable use cases across various industries. 

Facial Recognition to Beer Tasting: Akida Neuromorphic Processor in Action

AI and robotics are enabling safer and more efficient space exploration. 

Embracing the Cosmos: Akida's Role in Advancing Space Tech for Daily Life

<p dir="ltr" id="isPasted">BrainChip Holdings Ltd is a company that specializes in neuromorphic computing. They're known for their innovative approach and strategic vision in the field of artificial intelligence (AI). Their technology aims to redefine how AI is implemented at the edge. This article shares the highlights from their latest investor podcast to provide a holistic view of the company's achievements, strategies, and future direction.</p><h4 dir="ltr">A Vision for the Future</h4><p dir="ltr">BrainChip is committed to advancing neuromorphic computing, a cutting-edge field that mimics the neural architecture of the human brain. This approach promises unprecedented efficiency and performance in AI applications, particularly in edge computing scenarios where power and speed are critical.&nbsp;</p><p dir="ltr">The recent addition of Dr. Tony Lewis to the BrainChip leadership team underscores this commitment. With a rich background in AI, engineering, and neuromorphic technologies, Dr. Lewis brings a wealth of knowledge and a clear vision for the company's future.</p><p dir="ltr">n this journey. With an illustrious career spanning AI, engineering, and neuromorphic technologies, Dr. Lewis's expertise is set to steer BrainChip towards new horizons.</p><p dir="ltr">The announcement of Dr. Lewis joining the team heralds a new era for BrainChip, emphasizing the company's dedication to leading the edge in AI technology. Dr. Lewis brings a profound understanding of brain-inspired computing and robotics, with a track record of leading large research groups and pioneering innovations in the field. His appointment is not just a testament to BrainChip's current leadership in neuromorphic computing but also a clear indicator of its ambitious roadmap ahead.</p><p dir="ltr">Dr. Lewis's background is particularly noteworthy, with a PhD in brain-inspired computing in robotics, positioning him as a perfect fit for BrainChip's mission. His career has been a journey towards this role, marked by significant achievements in neomorphic and robotics research funded by the US government and industrial partners. His experience spans across defense, space, biomedical, environmental monitoring, mobile phones, and personal computers, highlighting a versatile and impactful career.</p><p dir="ltr">At Qualcomm, Dr. Lewis led the largest research project outside of wireless in corporate R&amp;D, focusing on brain-inspired computing. His foresight into the potential of deep learning and language models has been pivotal, anticipating the significant impact these technologies would have on the industry. His role at HP as the Global Head of the AI and Emerging Compute Lab further honed his skills, preparing him for the challenges and opportunities of being BrainChip's CTO.</p><p dir="ltr">Dr. Lewis's decision to join BrainChip was driven by the recognition of AI's golden age and the critical role of edge computing in shaping the future. He acknowledges the challenges of cloud processing, including its massive energy consumption and latency issues, underscoring the necessity for efficient, edge-based solutions. BrainChip, with its focus on neuromorphic computing, represents the cutting edge of this transition, offering solutions that are not only technologically advanced but also environmentally conscious and cost-effective.</p><h4 dir="ltr">Strategic Market Position and Competitive Edge</h4><p dir="ltr">BrainChip's belief in AI as a significant economic driver is matched by its strategic focus on edge computing. The company's neuromorphic computing platform offers a compelling alternative to traditional AI solutions, providing extreme efficiency, low power consumption, and real-time processing capabilities. This positions BrainChip uniquely in a market that is increasingly moving towards decentralized, edge-based computing solutions.</p><p dir="ltr">The competitive landscape of AI and neuromorphic computing is diverse, with several players vying for dominance. However, BrainChip sets itself apart through its comprehensive platform that supports a wide range of AI models and applications. The company's IP business model, which includes licensing fees and royalties, offers flexibility and scalability to partners and clients, further solidifying its market position.</p><h4 dir="ltr">Innovations and Achievements</h4><p dir="ltr">One of the key highlights from the investor podcast was BrainChip's focus on continuous innovation and product development. The introduction of the AKD1000 processor and the development of the next-generation IP are testament to the company's commitment to advancing neuromorphic computing. These technologies enable a broader range of use cases, from industrial automation to consumer electronics, by supporting more complex AI models and providing easier integration with existing workflows.</p><p dir="ltr">BrainChip's success is also reflected in its strategic partnerships and collaborations. The company has established relationships with industry leaders, research institutions, and technology developers to enhance its product offerings and expand its market reach. These partnerships not only validate BrainChip's technology but also provide valuable feedback and insights that drive further innovation.</p><h4 dir="ltr">Looking Ahead: Challenges and Opportunities</h4><p dir="ltr">As BrainChip continues to navigate the complex landscape of AI and neuromorphic computing, it faces both challenges and opportunities. The fragmented nature of the edge AI market presents a significant challenge, requiring a nuanced understanding of customer needs and industry trends. However, BrainChip's flexible business model and customer-focused approach position it well to adapt and thrive in this dynamic environment.</p><p dir="ltr">The potential competitive threat from major players like Nvidia moving into the edge computing space underscores the importance of maintaining a competitive edge through innovation and strategic partnerships. BrainChip's commitment to supporting standard machine learning workflows and its unique on-chip learning capabilities provide a strong foundation for sustaining its market leadership.</p><h4 dir="ltr">Conclusion</h4><p dir="ltr">BrainChip's journey in the AI industry is marked by bold visions, strategic innovations, and a relentless pursuit of excellence. The company's focus on neuromorphic computing not only sets it apart from competitors but also positions it as a pioneer in the future of AI. With a strong leadership team, strategic partnerships, and a commitment to continuous innovation, BrainChip is poised to lead the generational shift towards efficient, powerful AI at the edge. As the company navigates the challenges and opportunities ahead, its contributions to the field of AI and neuromorphic computing will undoubtedly continue to shape the technological landscape for years to come.</p>

Highlights from BrainChip's latest investor podcast provide a view of the company's achievements, strategies, and future direction.

BrainChip Podcast: Shareholder and innovation update

<p>BrainChip Holdings Ltd. has recently announced that it has opened pre-orders for the <a href="https://shop.brainchipinc.com/products/akida%E2%84%A2-edge-ai-box" target="_blank" id="isPasted">Akida Edge AI Box,</a>&nbsp; a significant milestone in the field of Edge AI. Through collaboration with VVDN Technologies, BrainChip has made significant progress in developing high-performance and energy-efficient artificial intelligence solutions that can be applied to various industries and use cases.</p><h4 dir="ltr" id="isPasted">Unveiling the Power of the Akida Edge AI Box</h4><p dir="ltr">The Akida Edge AI Box is engineered to harness the potential of neuromorphic computing, an innovative approach that mimics the human brain's neural networks. This technology enables the device to perform complex cognitive tasks more efficiently, using significantly less power than traditional computing methods.</p><p dir="ltr">Designed for versatility, the<a href="https://shop.brainchipinc.com/" target="_blank">&nbsp;Akida Edge AI Box&nbsp;</a>finds its application across various sectors, including retail, security, smart cities, automotive, and industrial processes. It aims to offer a solution that not only reduces operational costs but also enhances data privacy and security by processing data on the device, thereby minimising the need for constant cloud connectivity.</p><h4 dir="ltr">Key Features and Applications</h4><ul><li dir="ltr"><p dir="ltr">Ultra-Low Power Consumption: The device operates on minimal power, making it ideal for deployment in energy-sensitive environments.</p></li><li dir="ltr"><p dir="ltr">High-Performance AI Capabilities: With the ability to handle high-level computations and process vast amounts of data, the Akida Edge AI Box is perfect for complex applications like image processing and real-time analytics.</p></li><li dir="ltr"><p dir="ltr">Adaptability and Learning: Mimicking the plasticity of the human brain, the device can learn from new information and adjust its processing accordingly, enhancing its efficiency over time.</p></li><li dir="ltr"><p dir="ltr">Real-Time Processing: The Akida Edge AI Box excels in scenarios requiring immediate data analysis and decision-making, such as autonomous vehicles and smart surveillance systems.</p></li><li dir="ltr"><p dir="ltr">Robust and Resilient: Engineered to be fault-tolerant, the device ensures reliable operation even in challenging environmental conditions.</p></li></ul><h2 dir="ltr">Technical Specifications</h2><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709550646582-Screenshot+2024-03-04+121015.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr" id="isPasted">Priced at $799, the Akida Edge AI Box not only stands out for its technological prowess but also for its affordability, making advanced neuromorphic computing accessible to a broader range of industries.</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5NTUwNDI5NjI5LWJyYWluYm94LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">The Akida is best in class compared to current market products. Image credit: BrainChip.</span></span></span></p><h1 dir="ltr" id="isPasted">Bringing Edge AI Everywhere: Application examples</h1><h3 dir="ltr">Security and Surveillance</h3><p dir="ltr">the Akida Edge AI Box's capabilities have the potential to significantly transform the way monitoring and threat detection are carried out..&nbsp;Its ability to process data locally, with ultra-low latency, ensures real-time analysis of video feeds, detecting anomalies, unauthorized access, or suspicious behaviors instantly. This rapid processing can significantly enhance security measures in both public and private spaces, offering a more robust response to potential threats.</p><h3 dir="ltr">Smart Factories</h3><p dir="ltr">For smart factories, the Akida Edge AI Box brings the potential for improved automation, predictive maintenance, and operational efficiency. By processing data on-site, it can monitor equipment health in real-time, predict failures before they occur, and optimize manufacturing processes. This not only reduces downtime but also extends the lifespan of machinery, leading to significant cost savings and increased production efficiency.</p><h3 dir="ltr">Smart Retail</h3><p dir="ltr">In smart retail environments, the Akida Edge AI Box can personalize the shopping experience through real-time analytics. It can manage inventory, optimize store layouts based on customer behavior analytics, and offer targeted promotions to customers. This level of personalization and efficiency can enhance customer satisfaction and loyalty, driving sales and improving retail operations.</p><h3 dir="ltr">Smart Cities</h3><p dir="ltr">The Akida Edge AI Box can play a pivotal role in developing smart city infrastructure, from traffic management and waste management to energy conservation. Its capacity for edge processing allows for the real-time analysis of data from various sensors across the city, enabling intelligent decision-making that can improve city services, reduce congestion, enhance public safety, and lower environmental impact.</p><h3 dir="ltr">Warehouses</h3><p dir="ltr">In warehouses, the Akida Edge AI Box can streamline operations by optimizing logistics, managing inventory in real-time, and automating the tracking and dispatching of goods. This not only improves operational efficiency but also reduces the likelihood of errors, ensuring that the right products are always in stock and delivered on time.</p><p dir="ltr">By bringing AI processing capabilities directly to the edge, the Akida Edge AI Box enables these sectors to operate more efficiently, safely, and sustainably. Its introduction represents a significant step forward in the deployment of intelligent, secure, and customized devices and services for multi-sensor environments, catering to the specific needs of these diverse applications.</p><h2 dir="ltr">The Future of Edge Computing</h2><p dir="ltr">The collaboration between BrainChip and VVDN Technologies in developing the Akida Edge AI Box is a testament to the power of strategic partnerships in driving technological innovation. This device is set to redefine the landscape of Edge computing, ushering in a new era of intelligent, efficient, and autonomous systems.</p><p dir="ltr">By addressing the critical challenges of energy consumption, processing power, and data privacy, the Akida Edge AI Box is poised to play a crucial role in the evolution of Edge AI. Its launch not only meets the current market demand for more efficient computing solutions but also lays the groundwork for the future of intelligent devices and systems across a multitude of sectors.</p><p dir="ltr">As industries continue to seek smarter, more efficient technologies, the Akida Edge AI Box stands as a beacon of progress in the realm of Edge computing, promising a future where artificial intelligence is both profoundly intelligent and sustainably efficient.</p><p dir="ltr"><a href="https://shop.brainchipinc.com/" target="_blank">Order the&nbsp;Akida Edge AI Box here.</a></p>

BrainChip Holdings Ltd. Initiates Pre-Orders for the Akida Edge AI Box, Elevating Edge AI Capabilities through a Strategic Alliance with VVDN Technologies

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